Image_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

“…Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. …”

Presentation_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

“…Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. …”

Presentation_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

“…Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. …”

Table_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

“…Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. …”

Supplementary Table 7 from Integrative Genomic Analysis Predicts Causative <i>Cis</i>-Regulatory Mechanisms of the Breast Cancer–Associated Genetic Variant rs4415084 by Yi Zhang (9093)

Test function information. by Chuang Zhang (2199421)

“…By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. …”

Samples from IPPs with a mixture of squared exponential intensity function (<i>p</i> = 2). by M. Hassan Tanveer (9596437)

Detailed schematics of the algorithm developed to map and filter protein lists from one organism to another (ortholog identification). by Silvia Ambrós (11037668)

Signal detection algorithm adapted from [1] yields exponential distributions and unrealistic mean durations of percepts. by Quynh-Anh Nguyen (847240)

“…(Bottom) Trial-by-trial applications of the signal detection algorithm from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>] with A: <i>C</i>_<i>th</i> = 4.01 and B: <i>C</i>_<i>th</i> = 4.21 yield exponentially distributed subsequent percept durations for <i>I</i> (blue) and <i>S</i> (red). …”

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

Subjects:

Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy by Ayla Arslan (17943365)

“…</p> <p>The study employs a combination of in silico algorithms to analyze 82 variants of unknown clinical significance of GABRD gene sourced from the ClinVar database. …”

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

“…Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. …”

CEC2017 basic functions. by Tengfei Ma (597633)

“…The optimal individual’s position is updated by randomly selecting from these factors, enhancing the algorithm’s ability to attain the global optimum and increasing its overall robustness. …”

An Algorithmic Approach Based on Data Trees and Genetic Algorithms to Understanding Charged and Neutral Metal Nanocluster Growth by Chelsea M. Mueller (8351745)

“…We present a data tree-based approach to mapping these reaction pathways based on structures and energies obtained from Density Functional Theory (DFT) computations and including positive, negative, and neutral clusters in a continuum solvent of water. …”

The main steps of the general algorithm. by Dror Mughaz (18017583)

Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems by Dongge Lei (6836915)

“…The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed.…”

Test function results. by Ruyi Dong (9038174)

“…From the statistical results of mean, best and variance of different algorithms, the LLSKSO algorithm outperforms the other algorithms. …”

Benchmark test functions. by Ruyi Dong (9038174)

“…From the statistical results of mean, best and variance of different algorithms, the LLSKSO algorithm outperforms the other algorithms. …”

GraSPy: an Open Source Python Package for Statistical Connectomics by Benjamin Pedigo (6580352)

“…GraSPy builds on Python’s existing graph and machine learning ecosystem by accepting input from NetworkX and complying with the scikit-learn API. …”